In image processing, error diffusion may be used to render a continuous tone image on a binary output marking engine (e.g., a copier, printer, or the like). Error diffusion may be used in multi-function devices (MFDs) to render print-ready bitmaps from scanned monochrome and/or color images. Error diffusion generally provide favorable rendering features without generating artifacts (e.g., moiré artifacts or the like). Furthermore, error diffusion provides a good compromise relative to image quality when processing documents with mixed content since it preserves the image density of photographs rendering text, line-art, and graphics with relatively good results.
However, due to the overall spatial frequency response or modulation transfer function (MTF) of many scanners, the edges of objects and text contained within a scanned RGB (red-green-blue color space) contone image are blurred, resulting in edge-degradation within the printed text. In other words, a lower text-quality may be rendered, especially along edges, via error diffusion and therefore deemed objectionable by some consumers who demand high print image quality.
For example, in a synthetically generated contone 8-bits/pixel (8 bpp) image (e.g., a computer generated image), an edge may be “perfect” such that it abruptly transitions from white (e.g., a value of 0) to black (e.g., a value of 255) across an edge without intervening values. For example, such a perfect edge may be provided at a text boundary or a line boundary or the like. However, in a scanned contone image with a typical modulation transfer function of the same or similar text boundary or line boundary or the like, the scanned edge may transition from white (0) to black (255) with intervening values (between 0 and 255) having a sigmoid type shape or the like.
In generating a print ready binary image from such a scanned image, for example, the image processing technique may have to choose between white or black (in binary images) or a limited range of options (e.g., intensity of 0 to 3 in two bit implementations) for such transition values. In one error diffusion algorithm (e.g., the Floyd & Steinberg algorithm), after choosing the value of the current pixel (from the limited options as discussed), the generated error component is diffused to the downstream neighboring pixels. For example, the generated error may be determined as the difference between the desired and printed values. In such a case, the desired value is the input gray pixel level (e.g., the pixel value from the scanned contone image) and the printed value is either 255 or 0 (in the binary example). Since the 8 bpp scan image is typically blurred relative to an ideal or synthetic image (as discussed), the error that is generated within these regions when processed via error diffusion may result in sporadic pixels around these (edge) transitions. For example, from a high-level visual perspective, the edges of text and lines are therefore not as sharp as desired.
Precise and well-defined edges of black text and line-art is an important rendering feature expected or desired by customers using high-resolution printers and MFDs. It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to attain from scanning and subsequently print high quality images becomes more widespread.